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ADVANCED SATELLITE-BASED OIL SPILL DETECTIONSPILL DETECTION
INSTITUTO HIDROGRÁFICO, LISBON, 24-26 JUNE 2014
Andrea Radius*1, Rui Azevedo*1, Delfim Rego*1 , Paulo Carmo*1
*1 Edisoft, Paço de Arcos, Portugal
Maritime awareness
• Ocean health is crucial for the Earth well being
• The ocean is a eco-system with major responsibility
for climate, coastal erosion, etc
• Maritime pollution monitoring became an essential
tool to monitor sea health and a very important
challenge in terms of natural environmental
preservation
Maritime Monitoring context
• Sea pollution monitoring importance is stressed by
several European (and not only) projects that aim on
sea preservation
• The synergy between R&D and operational
projects is crucial to implement global monitoring projects is crucial to implement global monitoring
architectures in the near future
• EDISOFT works on the bigger European operational
project of pollution monitoring: EMSA CleanSeaNet
• Other important R&D projects which EDISOFT have
worked: EC FP7 Sea-U and ESA AOSD
•
• The knowledge acquired during the aforementioned
projects allowed EDISOFT to plan the implementation of
a non-supervised automatic tool for oil spill detection
AOSD context
a non-supervised automatic tool for oil spill detection
• The project involves strong R&D components to
improve oil spill detection capabilities
• Convergence to an automatic algorithm
• Being closely related to the operational segment
• Focused on the oil spill detection processing chain
automation (e.g. CleanSeaNet)
• Integration with an already tested and optimized
AOSD objectives
• Integration with an already tested and optimized
software as NEST
• Integrating auxiliary data (like wind data), together
with image and geometry analysis
Software architecture
Level 1BLand
Mask
Data
Reader
Filter
WindsWind
The AOSD follows the processing model of NEST; its
architecture is the following.
Statistical
Analysis
Dark
Pixels
Detection
Vectorize
Clusters
Cluster
Dark
Pixels
Wind
Trainset
Contours
Filtering
Features
Extraction
ClassifyOil Spill
Report
Data reader
The current software version reads data acquired
from the following satellites:
• Envisat
• Radarsat-1
• Radarsat-2• Radarsat-2
• Imagery data can be combined, if available, with
winds ancillary data
Land mask
• Land areas are masked with no data value, to be
excluded by the detection component
• A user defined buffer is added to exclude areas
near the coast
Filter winds
• Invalid wind areas are excluded from the analysis
• “Invalid” is defined according to the user input
criteria
� Low winds – lower � Low winds – lower
values are excluded
� High winds – higher
values are excluded
Statistic analysis
• Smaller window areas are analyzed to find
individual threshold values
• Threshold values are automatically adapted to a
sliding-window covering each analysed area
� Window size – defined by � Window size – defined by
the user
� Statistic data extraction – in
each sliding-window
� Threshold – combining
statistical data
Dark pixels detection
• Each pixel is filtered according to the established
window threshold
• Only lower amplitude values allow the analysed
pixel to move on to the next phase
Cluster dark pixels
• Darker pixels are
aggregated according to
a user defined distance
• Each group of
aggregated pixels form aggregated pixels form
a cluster
• Non-clustered pixels
are discarded
Vectorize clusters
• Clusters are
vectorised to
polygons
• The remaining dark clusters are converted from
raster to the vectorial domain
• Polygons are
simplified to
contours
polygons
Contours filtering
• Formed contours can be discarded if they do not
comply with three exclusion criteria:
1. Being an invalid polygon1. Being an invalid polygon
2. Dimension
3. Closeness to an invalid wind area
Features extraction
• Well defined features that can better characterise
an oil spill were previously chosen
• Based on the definition, the individual features of
each contour are extracted
• The individual feature values are delivered to the
classifier
Classify
• All detected and filtered remaining patterns are
then classified based on their individual features
• Classification is done in two levels
Contrast
1st Level 2nd Level
Features
Spill
Look-alike
Contrast
Type
Shape
Surround
Edges
Classify
• The first classification level is based on the Support
Vector Machines (SVM).
It has the purpose to classify the detected dark
patch as oil spill or look-alike.
• The second level of classification is executed for
the classification of the detected oil spills, with the
purpose to characterize the oil spill according to its
attributes
Oil Spill Report
• Available in three different formats:
1. Esri shapefile
2. EMSA OSN
3. Google Earth KMZ
Correlation analysis
• Classification can only provide efficient predictions if the model is well balanced
• Development of a prediction model capable of classifying dark features as a spill or a look-alike
• It was essential to determine which variables are relevant to the model
• Understanding each variable relevance was performed through a correlation analysis
Oil spill characteristics
• Type – linear, tail, angular, patch, droplet
• Contrast – strong, medium, weak
• Surround – homogeneous, inhomogeneous
• Shape – smooth, irregular
• Edges – Sharp, sharp and diffuse, diffuse
• Wind speed – low, moderate, high
Extracted variables
• The variables are extracted according to the
aforementioned oil spill characteristics
• Definition of each detection with the variables:
� Area
� Perimeter
� Length
� Width
� Linearity
� Complexity
� Density
� Gradient
Decision variable
• Spill confidence level
• Divided in five sub-level types:
� Ongoing spill
� High confidence
� Medium confidence
� Low confidence
� Lookalike
• The variable holding the prediction result
Results
• Prepared dataset based on the available archive
• Total of 78 ENVISAT scenes randomly selected
• AOSD validation data set results:
AOSD Lookalike Low Medium High Total
Success 18585 76 71 41 18773
Fails 3003 56 58 30 3147
Rating 86% 58% 55% 58%
Overall performance 86%
Detection example I
Detection example II
CSN results (2007-2011)
Future work
• Upgrade to the latest available version of NEST
• Integration of AOSD within the EMSA CSN service
• Integration of other SAR systems – Sentinel-1,
TerraSAR-X, COSMO-SkyMed
• Spill orientation – to detect possible responsible ships• Spill orientation – to detect possible responsible ships
• Integrate other ancillary data sources – to help
preventing false positive detections
• Improve time efficiency – to be used operationally
• Incorporate additional layers of information – as
detected vessels and AIS positions
THANK YOU
Rua Calvet de Magalhães, N245
2770-153 Paço de Arcos
Portugal
TEL: +351 212 945 900
FAX: +351 212 945 999
www.edisoft.pt